Job Description
The Senior ML Engineer, Energy is responsible for developing and optimizing machine learning models and trading strategies for renewable energy assets, focusing on solar storage and energy market forecasting, to enhance profitability and support energy infrastructure projects.
Key Responsibilities
- Develop, maintain, and improve machine learning models for solar storage trading tools in CAISO and ERCOT markets.
- Implement data science and ML best practices for automated algorithmic trading, backtesting, and benchmarking.
- Collaborate with portfolio management, power marketing, and risk teams to develop and optimize solar storage trading strategies.
- Support backend development for energy management system optimization in SB Energy's test facilities.
- Develop data center hourly power modeling products using renewable energy resources.
- Create AI agents to analyze trading strategy performance, backtest strategies, and adjust bids/offers based on trade results.
Requirements
- Bachelor's Degree in a quantitative discipline e.g., Electrical and Computer Engineering, Computer Science, Statistics, Math, Operations research or related field.
- Proficient with Python.
- Strong working experience with Python data science and machine learning packages, such as NumPy, Pandas, Matplotlib, Plotly, Scipy, stats model, XGBoost, Scikit-Learn, Keras, TensorFlow, PyTorch, pyomo, OR-tools etc.
- The candidate is familiar with the technical aspects of solar storage modeling for CAISO and ERCOT markets, power systems, including power flow modeling, basis, congestion, transmission network, and the impact of generation solar, wind, gas, coal, etc. and load on LMPs.
- Experienced individual with expertise in optimization, multivariate time series forecasting, stochastic modeling and optimization, and Monte Carlo simulations.
- Support energy trading, congestion management, and battery analytics.
- 5-8 years of ML engineering experience.
- Preferably 2-3 years of experience in the energy market.
- Ability to develop, maintain, and improve solar storage trading tools using machine learning pipelines for CAISO and ERCOT markets.
- Ability to implement data science and ML engineering best practices in automated algorithmic trading, backtesting, and benchmarking use cases.
- Ability to collaborate with Portfolio management, power marketing, and Risk teams to develop solar storage trading strategies for resource adequacy (RA), merchant arbitrage, and ancillary services, including real-time backtesting for optimal revenue and understanding of risk-reward of various strategies.
- Ability to develop data center hourly shaped power modeling products using renewable energy resources.
- Ability to develop AI agents to analyze trading strategy performance, backtest strategies, and adjust bids/offers based on trade results for solar storage DART, PTP, ancillary services, and 24/7 modeling strategies.
Benefits & Perks
Base salary range of $140,000 to $180,000
Discretionary annual bonus
Long-term incentive plan
100% Company-Paid Medical, Dental, and Vision insurance for employees and dependents
401(k) plan with Company Match
Generous Paid Time Off
11 Paid Holidays
12 Weeks Paid Parental Leave
Life, AD&D, Long-Term Disability Coverage
Flexible Spending Accounts (FSA) for Medical, Dependent Care, Transit
Parking with company contributions
Flexible Work Arrangements including Hybrid Office Setup
Monthly Reimbursement for Phone and Internet Data
Optional Legal Pet Insurance Plans
Device Purchase Support
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